1. Field of the Invention
The invention relates to a method for monitoring the depth of anesthesia, and in particular, a method for monitoring the depth of anesthesia based on the theory of approximate entropy.
2. Description of the Prior Art
Anesthesia is an indispensable part of surgery. In the course of operation, either over or under dosage of anesthetics will cause adverse effects on the patient. As a traditional anesthesia method, an anesthesiologist monitors depth of anesthesia in a patient under anesthesia based on observations on the underlying change of physiological symptoms such as breathing rates, the blood pressure, the heart beat, eye signs and the like as well as the patient's physical response to stimulation caused by the operation procedure. However, in the process of anesthesia, a muscle relaxant might be used as an auxiliary drug to have better muscle relaxant effect on the patient during the surgical operation. A muscle relaxant itself exhibits neither an analgesic action nor an anesthesia action. Furthermore, since patients under such situations cannot breathe by themselves and are inactive, a false perceived depth of anesthesia may be consequently produced. Thereby, the anesthesiologist might lose an important objective estimation criterion, and hence could not readily monitor or detect the true anesthesia state of a patient.
In recent years, owing to the research and analysis on electroencephalogram (EEG), a dramatic progression on the determining of depth of anesthesia has occurred. The principle of brain wave measurement relies on the vertical arrangement of pyramidal neurons distribution in the human cerebral cortex. The dendrite or cell body (or soma) in these pyramidal neurons can generate local potential variation during activity, i.e., the so-called physiological potential. These potential variations can be recorded by attaching electrodes to the patient. The physiological potential of the brain wave is generally very weak, approximately at 5-30 μV, and resides in the type of alternative signal of 0.5-60 Hz. Based on the difference of frequency, EEG can be classified into 4 types: Delta wave (0.5˜4 Hz), Theta wave (4˜8 Hz), Alpha wave (8˜13 Hz), and Beta wave (13˜32 Hz).
Alpha wave appears as the main brain wave when a patient is at a static state, during rest, and eyes closed, and it disappears as eyes open. Beta wave occurs often in the period of strong mental activity. Theta and delta waves are associated with sleep and brain pathology. Clinically, brain wave characteristics can be utilized in the diagnosis or understanding of the electrical discharge of the cranial nerve cell. For example, in case of epilepsy, brain tumor, or brain injury, an abnormal discharging cranial nerve cell might evoke a synchronous electrical discharge by surrounding cranial nerve cells. Upon signal transmitting and aggregating, a distinct spike signal will occur. By virtue of the feature of a multiple point cranial nerve wave, an abnormal discharging location can be deduced. In the phase of sleep, the brain wave will exhibit some special wave forms such as k-complex and sleep spindle. In the recovery course after a brain damage or oxygen deficiency, the brain wave will present a feature of burst suppression. Based on the relatively complex feature presented in the brain wave, research can be made in terms of a frequency domain and a time domain.
In the aspect of frequency domain analysis, Schwilden and Stoeckel[1] investigated the energy distribution of the delta, theta, alpha, and beta waves in patients injected with isofurane, using fast Fourier transformation (FFT). They have found that, prior to injecting the patient with isofurane, a higher expression of the energy of beta wave occurred. However, after injecting the patient with isofurane, the energy of alpha wave became higher while the energy of beta wave decreased. Accordingly, it was suggested that the degree of consciousness of the patient could be correlated with energy of alpha and beta waves. Katoh et al.[2] analyzed the median frequency energy distribution of patients as they received sevoflurane anesthesia by using median edge frequency (MEF) and spectral edge frequency 95 (SEF95). MEF theory defines the energy distribution change below 50% of total energy as the brain wave is in the frequency domain range of 0.5-30 Hz. The SEF95 theory defines the energy distribution change below 95% of total energy as the brain wave is in the frequency domain range of 0.5-30 Hz. Katoh et al. have found that changes of SEF95 and MEF could be correlated intimately with the concentration of sevoflurane. As the concentration of sevoflurane increased, energy distributions both of SEF95 and MEF would tend to be low. On the other hand, as the concentration of sevoflurane decreased, both energy distributions would increase. However, when the electric resistivity on the skin of a patient is high, the predictability from both of SEF95 and MEF is poor. Miyashita et al.[3] studied changes of the brain wave and heart beat variation during sleeping as well as in conscious state. They analyzed brain wave in terms of SEF50, SEF90, and SEF95, and also analyzed the low frequency/high frequency (LF/HF) ratio for the heart beat variation by using FFT. The study pointed out that when people were sleeping, the energy distributions of SEF50, SEF90 and SEF95 tended to be lower than those in consciousness, and the variation of the value of SEF95 is the most significant one among them. Furthermore, the LF/HF ratio in sleep tended also to be less than the LF/HF ratio in consciousness. Billard et al.[4] analyzed the degree of anesthesia for patients who received different anesthetics such as alfentanil, propofol, and midazolam through SEF95, Delta Power, and Bispectrum Index (BIS). The study revealed that, no matter what anesthetic, either alfentanil, propofol or, midazolam, were received by a patient, BIS can distinguished equally well whether the patient is in consciousness or in anesthesia state, while SEF95 can only differentiate conscious states between patients received propofol and midazolam.
In the aspect of time domain, Elbert et al.[5] and Pritchard and Duke et al.[6] believed that a brain wave signal was not composed of a sine wave, rather the brain wave signal was a disorderly and confused, irregular signal. Therefore, they proposed the analysis of brain wave signal by means of a nonlinear method. Fell et al.[7], Grassberger and Procaccia et al.[8] as well as Eckmann and Ruelle[9] analyzed the regularity of a nonlinear signal by using different types of entropy. Till 1991, Bruhn et al.[10] proposed the application of approximate entropy (ApEn) on the nonlinear analysis of physiological signal. Furthermore, Yeragania et al.,[11] collected brain wave signals from patients under desflurane anesthesia, and analyzed the regularity within these signals by approximate entropy. They revealed that brain waves of patients displayed an irregular change before anesthesia while exhibited a regular change after anesthesia. In addition, approximate entropy is applied frequently for the differentiation of diseases. For example, Diambra et al. [12] tried to analyze EEG signals from healthy people and patients with epilepsy by approximate entropy. They have found that the value of approximate entropy from patients with epilepsy was less significantly than that of healthy people. Suchuckers[13] used approximate entropy instead of standard deviation analysis to distinguish the difference of heart beat between ventricular fibrillation and non-ventricular fibrillation, because the traditional standard deviation analysis failed to observe the regularity of a signal and also could not differentiate effectively a disease. The results indicated that patients with ventricular fibrillation and ventricular tachycardia had a value of approximate entropy significantly higher than that of normal people.
At present, to monitor and detect the depth of anesthesia, other than basing on one's experience, an anesthesiologist also monitors and detects depth of anesthesia based on some of the most commonly used methods such as Bispectrum Index (BIS) and Auditory Evoked Potential (AEP). Both of these methods measure the EEG of the anesthetized subject. BIS is based on Bispectrum and in conjunction with the anesthesia consciousness index induced from a great deal of patient data. The theory underlying BIS has not been publicly disclosed yet. On the other hand, AEP makes use of one's auditory response to measure the depth of anesthesia of a patient, since the auditory function is the sensory function that is restored first, and the lost last, in the course of anesthesia. In addition, the brain wave at middle latency is associated with anesthesia and its measuring method comprises of stimulating the one being test with a 6 Hz sound wave. Immediately after completion of each sound stimulation, the instrument takes a brain wave sample of 120 ms with a sampling frequency of 1 kHz. Thereafter, it calculates the average value of these 120 ms data. Nevertheless, since BIS and AEP equipments are expensive, they are not widely available in every operation room and in every hospital. Moreover, since its theory has not been fully disclosed, physicians cannot effectively master material information to monitor and detect depth of anesthesia, which in turn may result in anesthesia of a patient that is of too deep or too shallow. Such undesirable situation increases the risk in the operation.
Accordingly, the methods for predicting depth of anesthesia mentioned above have many disadvantages, and they are not perfect designs and need to be improved urgently.
In view of various disadvantages derived from the conventional methods for predicting depth of anesthesia mentioned above, the inventors have devoted to improve and innovate, and after intensive studying for many years, they developed finally and successfully a method for predicting depth of anesthesia, thereby accomplished the invention. All referenced patent and non-patent prior art are incorporated herein by reference in their entirety.